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"""Decoder for CTC-based ASR.""" ""
import os
from dataclasses import dataclass
import torch
from torchaudio.datasets.utils import _extract_tar
from torchaudio.models.decoder import ctc_decoder
from swr2_asr.utils.data import create_lexicon
from swr2_asr.utils.tokenizer import CharTokenizer
@dataclass
class DecoderOutput:
"""Decoder output."""
words: list[str]
def decoder_factory(decoder_type: str = "greedy") -> callable:
"""Decoder factory."""
if decoder_type == "greedy":
return get_greedy_decoder
if decoder_type == "lm":
return get_beam_search_decoder
raise NotImplementedError
def get_greedy_decoder(
tokenizer: CharTokenizer, # pylint: disable=redefined-outer-name
*_,
):
"""Greedy decoder."""
return GreedyDecoder(tokenizer)
def get_beam_search_decoder(
tokenizer: CharTokenizer, # pylint: disable=redefined-outer-name
hparams: dict, # pylint: disable=redefined-outer-name
):
"""Beam search decoder."""
hparams = hparams.get("lm", {})
language, lang_model_path, n_gram, beam_size, beam_threshold, n_best, lm_weight, word_score = (
hparams["language"],
hparams["language_model_path"],
hparams["n_gram"],
hparams["beam_size"],
hparams["beam_threshold"],
hparams["n_best"],
hparams["lm_weight"],
hparams["word_score"],
)
if not os.path.isdir(os.path.join(lang_model_path, f"mls_lm_{language}")):
url = f"https://dl.fbaipublicfiles.com/mls/mls_lm_{language}.tar.gz"
torch.hub.download_url_to_file(url, f"data/mls_lm_{language}.tar.gz")
_extract_tar("data/mls_lm_{language}.tar.gz", overwrite=True)
tokens_path = os.path.join(lang_model_path, f"mls_lm_{language}", "tokens.txt")
if not os.path.isfile(tokens_path):
tokenizer.create_tokens_txt(tokens_path)
lexicon_path = os.path.join(lang_model_path, f"mls_lm_{language}", "lexicon.txt")
if not os.path.isfile(lexicon_path):
occurences_path = os.path.join(lang_model_path, f"mls_lm_{language}", "vocab_counts.txt")
create_lexicon(occurences_path, lexicon_path)
lm_path = os.path.join(lang_model_path, f"mls_lm_{language}", f"{n_gram}-gram_lm.arpa")
decoder = ctc_decoder(
lexicon=lexicon_path,
tokens=tokens_path,
lm=lm_path,
blank_token="_",
sil_token="<SPACE>",
unk_word="<UNK>",
nbest=n_best,
beam_size=beam_size,
beam_threshold=beam_threshold,
lm_weight=lm_weight,
word_score=word_score,
)
return decoder
class GreedyDecoder:
"""Greedy decoder."""
def __init__(self, tokenizer: CharTokenizer): # pylint: disable=redefined-outer-name
self.tokenizer = tokenizer
def __call__(
self, output, greedy_type: str = "inference", labels=None, label_lengths=None
): # pylint: disable=redefined-outer-name
"""Greedily decode a sequence."""
if greedy_type == "train":
res = self.train(output, labels, label_lengths)
if greedy_type == "inference":
res = self.inference(output)
res = [x.split(" ") for x in res]
res = [[DecoderOutput(x)] for x in res]
return res
def train(self, output, labels, label_lengths):
"""Greedily decode a sequence with known labels."""
blank_label = tokenizer.get_blank_token()
arg_maxes = torch.argmax(output, dim=2) # pylint: disable=no-member
decodes = []
targets = []
for i, args in enumerate(arg_maxes):
decode = []
targets.append(self.tokenizer.decode(labels[i][: label_lengths[i]].tolist()))
for j, index in enumerate(args):
if index != blank_label:
if j != 0 and index == args[j - 1]:
continue
decode.append(index.item())
decodes.append(self.tokenizer.decode(decode))
return decodes, targets
def inference(self, output):
"""Greedily decode a sequence."""
collapse_repeated = True
arg_maxes = torch.argmax(output, dim=2) # pylint: disable=no-member
blank_label = self.tokenizer.get_blank_token()
decodes = []
for args in arg_maxes:
decode = []
for j, index in enumerate(args):
if index != blank_label:
if collapse_repeated and j != 0 and index == args[j - 1]:
continue
decode.append(index.item())
decodes.append(self.tokenizer.decode(decode))
return decodes
if __name__ == "__main__":
tokenizer = CharTokenizer.from_file("data/tokenizers/char_tokenizer_german.json")
tokenizer.create_tokens_txt("data/tokenizers/tokens_german.txt")
hparams = {
"language": "german",
"lang_model_path": "data",
"n_gram": 3,
"beam_size": 100,
"beam_threshold": 100,
"n_best": 1,
"lm_weight": 0.5,
"word_score": 1.0,
}
get_beam_search_decoder(tokenizer, hparams)
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